Statistics Expert

Alignerr Corp.; Seattle (WA); Remote; USD 34,000 - 138,000; Full time

Job summary

Labelbox is seeking a Math Expert (Contract) to support statistics-focused AI data labeling and content development. This fully remote role is US-friendly, paid hourly, and offers flexible, asynchronous collaboration across projects.

The ideal candidate has strong probability and statistics expertise, a pending or completed Bachelor's degree, and experience creating or reviewing educational data for AI training. Knowledge of RLHF is a plus.

Qualifications

• Deep expertise in probability and statistics through research, teaching, or applied problem solving.
• Bachelor’s degree or higher from an accredited institution.
• Experience building or reviewing complex data, educational, or technical content.
• Native-level proficiency in probability and statistics and clear written English communication.
• Bonus: Experience with data labeling, RLHF, or AI training projects.

Responsibilities

• Data annotation: label and categorize statistics content.
• Concept mapping: link probability topics and statistics concepts.
• Solution verification: review AI solutions and explain errors.
• Content development: build training datasets for statistics.
• Red teaming: test for biases and model robustness.

Skills

Probability & Statistics

Written English

Analytical thinking

Education

Bachelor’s degree or higher

Job description

• Remote (UnitedStates preferred) Shape the data that powers frontier AI

Math Expert (Contract)

Labelbox

• Remote (UnitedStates preferred) Shape the data that powers frontier AI

Quick facts

• Engagement - Hourly, at‑will contractor
• Schedule - Fully remote & asynchronous (min.15hrs/week)
• Pay Range (US) - \$25–\$100 per hour
• Start Date - Rolling — staffed as projects launch

What You’ll Do

• Data annotation: Accurately label and categorize statistical expressions, equations, datasets, problems, and related content
• Concept mapping: Connect statistical concepts and establish relationships between topics such as probability, distributions, hypothesis testing, regression, and statistical modeling to help AI models understand the underlying structure of statistics
• Solution verification: Analyze and provide feedback on AI-generated solutions to statistics and probability problems, identifying errors and offering step-by-step explanations to improve model accuracy
• Content development: Contribute to comprehensive training datasets covering a broad range of statistics concepts and difficulty levels
• Red teaming: Identify potential biases, inaccuracies, or limitations in the AI’s understanding of statistics and probability. Design tests to ensure AI outputs are robust and practically applicable

You’re a great fit if

• You have deep expertise in probability and statistics, demonstrated through research, teaching, or applied problem-solving
• You are enrolled in or have completed a Bachelor’s degree or higher from an accredited institution
• You have experience building or reviewing complex data, educational, or technical content
• You possess native-level proficiency in probability and statistics, and communicate concepts clearly in written English
• Bonus: Experience with data labeling, RLHF, or other AI training projects

About The Role

• Flexible workload — work from anywhere, on your own schedule
• High impact — your craft directly improves models used by top AI labs & Fortune500 teams
• Clear ownership — know exactly what success looks like and have autonomy to deliver
• Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs

Interview process

• Complete a screening with Zara, our AI interviewer in English, to learn more about your background and experience.
• Domain-specific Zara interview to assess your understanding of fundamental statistical principles, exploring the underlying mechanisms that govern data analysis and inference.

About Labelbox

Labelbox builds the data engine that accelerates breakthrough AI. Our platform, expert services, and marketplace let teams iterate on data as nimbly as they iterate on code, enabling safer, smarter models in production. We’re backed by SoftBank, AndreessenHorowitz, BCapital, GradientVentures, DatabricksVentures, and KleinerPerkins, and trusted by leading research labs and enterprises worldwide.